arXiv subjects

Yiming Ma

Publications and source records attributed to Yiming Ma.

At least 37 records · Page 2Linked to original sources

arXiv2025

Multidimensional Stochastic Dominance Test Based on Center-outward Quantiles

Stochastic dominance (SD) provides a quantile-based partial ordering of random variables and has broad applications. Its extension to multivariate settings, however, is challenging due to the lack of a canonical ordering in $\mathbb{R}^d$ ($d \ge 2$) and the set-valued character of multivariate quantiles. Based on the multivariate center-outward quantile function in Hallin et al. (2021), this paper proposes new first- and second-order multivariate stochastic dominance (MSD) concepts through comparing contribution functions defined over quantile contours and regions. To address computational and inferential challenges, we incorporate entropy-regularized optimal transport, which ensures faster convergence rate and tractable estimation. We further develop consistent Kolmogorov-Smirnov and Cramér- von Mises type test statistics for MSD, establish bootstrap validity, and demonstrate through extensive simulations good finite-sample performance of the tests. Our approach offers a theoretically rigorous, and computationally feasible solution for comparing multivariate distributions.

stat.ME↗

arXiv2025

CTransformer: Deep-transformer-based 3D cell membrane tracking with subcellular-resolved molecular quantification

Deep learning segmentation and fluorescence imaging techniques allow the cellular morphology of living embryos to be constructed spatiotemporally. These development processes involve numerous molecules distributed at the subcellular scale, such as cell adhesion (E-cadherin), which accumulate at cell-cell interfaces to regulate intercellular connection. However, quantifying molecular distributions within specific subcellular regions across the entire embryo, where cell movement and molecular redistribution occur rapidly, is challenging due to the need for simultaneous cell morphology reconstruction and lineage tracing due to photobleaching and phototoxicity. We report a transformer-based pipeline, CTransformer, that establishes a 4D cellular morphology map before the 550-cell (late) stage. CTransformer constructed 4D cellular morphology atlases, reaching 80% accuracy at the 550-cell stage. Through this advanced architecture, we use only one channel to reconstruct cell morphology and achieve cell tracing. With each cell's morphology as a reference, the distribution of specific molecules throughout the cell body and at cell interfaces can be quantitatively measured in another fluorescence channel. We apply this methodology to track E-cadherin during embryonic development of the worm Caenorhabditis elegans, from fertilization to gastrulation. Our results reveal that E-cadherin is tightly regulated across individual embryos, both within single cells and at cell-cell interfaces, displaying an anterior-posterior gradient and cell- and lineage-specific patterns. Furthermore, its spatiotemporal heterogeneity influences cell mechanics and embryonic morphogenesis, helping explain how C. elegans achieves stereotypical developmental patterns at cellular resolution.

physics.bio-ph↗

arXiv2025

Automated Detection of Short-term Slow Slip Events in Southwest Japan

Inferring from the occurrence pattern of slow slip events (SSEs) the probability of triggering a damaging earthquake within the nearby velocity weakening portion of the plate interface is critical for hazard mitigation. Although robust methods exist to detect long-term SSEs consistently and efficiently, detecting short-term SSEs remains a challenge. In this study, we propose a novel statistical approach, called singular spectrum analysis isolate-detect (SSAID), for automatically estimating the start and end times of short-term SSEs in GPS data. The method recasts the problem of detecting SSEs as that of identifying change-points in a piecewise non-linear signal. This is achieved by obscuring the deviation from piecewise-linearity in the underlying SSE signals using added noise. We verify its effectiveness on a range of model-generated synthetic SSE data with different noise levels, and demonstrate its superior performance compared to two existing methods. We illustrate its capability in detecting short-term SSEs in observed GPS data from 36 stations in southwest Japan via the co-occurrence of non-volcanic tremors, hypothesis tests and fault estimation.

stat.AP↗

arXiv2025

ZIP: Scalable Crowd Counting via Zero-Inflated Poisson Modeling

Most crowd counting methods directly regress blockwise density maps using Mean Squared Error (MSE) losses. This practice has two key limitations: (1) it fails to account for the extreme spatial sparsity of annotations - over 95% of 8x8 blocks are empty across standard benchmarks, so supervision signals in informative regions are diluted by the predominant zeros; (2) MSE corresponds to a Gaussian error model that poorly matches discrete, non-negative count data. To address these issues, we introduce ZIP, a scalable crowd counting framework that models blockwise counts with a Zero-Inflated Poisson likelihood: a zero-inflation term learns the probability a block is structurally empty (handling excess zeros), while the Poisson component captures expected counts when people are present (respecting discreteness). We provide a generalization analysis showing a tighter risk bound for ZIP than MSE-based losses and DMCount provided that the training resolution is moderately large. To assess the scalability of ZIP, we instantiate it on backbones spanning over 100x in parameters/compute. Experiments on ShanghaiTech A & B, UCF-QNRF, and NWPU-Crowd demonstrate that ZIP consistently surpasses state-of-the-art methods across all model scales.

cs.CV↗

arXiv2025

SLENet: A Novel Multiscale CNN-Based Network for Detecting the Rats Estrous Cycle

In clinical medicine, rats are commonly used as experimental subjects. However, their estrous cycle significantly impacts their biological responses, leading to differences in experimental results. Therefore, accurately determining the estrous cycle is crucial for minimizing interference. Manually identifying the estrous cycle in rats presents several challenges, including high costs, long training periods, and subjectivity. To address these issues, this paper proposes a classification network-Spatial Long-distance EfficientNet (SLENet). This network is designed based on EfficientNet, specifically modifying the Mobile Inverted Bottleneck Convolution (MBConv) module by introducing a novel Spatial Efficient Channel Attention (SECA) mechanism to replace the original Squeeze Excitation (SE) module. Additionally, a Non-local attention mechanism is incorporated after the last convolutional layer to enhance the network's ability to capture long-range dependencies. The dataset used 2,655 microscopic images of rat vaginal epithelial cells, with 531 images in the test set. Experimental results indicate that SLENet achieved an accuracy of 96.31%, outperforming baseline EfficientNet model (94.2%). This finding provide practical value for optimizing experimental design in rat-based studies such as reproductive and pharmacological research, but this study is limited to microscopy image data, without considering other factors like temporal patterns, thus, incorporating multi-modal input is necessary for future application.

eess.IV↗

arXiv2025

MHD simulation of tilt instability during the dynamic FRC magnetic compression process

The nonlinear evolution of the tilt instability in a field reversed configuration (FRC) during the dynamic magnetic compression process has been investigated using magnetohydrodynamic (MHD) simulations with the NIMROD code [C. R. Sovinec \textit{et al.}, J. Comput. Phys. \textbf{195}, 355 (2004)]. The tilt mode induces significant deformations in the linear growth phase and results in complete confinement loss of the FRC in the nonlinear phase, with no evidence of dynamic nonlinear stabilization. The growth rate of the tilt mode increases with the compression field ramping rate and approaches an asymptotic value. Toroidal flow can reduce both the growth rate and the nonlinear saturation amplitude of the tilt mode. The stabilizing effect of the toroidal rotation is enhanced with higher compression field ramping rates due to the spontaneous toroidal field generation and increased flow shear during compression. Although the tilt mode remains unstable with a toroidal rotation Mach number close to 0.5, the onset of tilt distortion can be delayed, allowing a magnetic compression ratio up to 5.3 before the compressional heating terminates.

physics.plasm-ph↗

arXiv2025

MDPE: A Multimodal Deception Dataset with Personality and Emotional Characteristics

Deception detection has garnered increasing attention in recent years due to the significant growth of digital media and heightened ethical and security concerns. It has been extensively studied using multimodal methods, including video, audio, and text. In addition, individual differences in deception production and detection are believed to play a crucial role.Although some studies have utilized individual information such as personality traits to enhance the performance of deception detection, current systems remain limited, partly due to a lack of sufficient datasets for evaluating performance. To address this issue, we introduce a multimodal deception dataset MDPE. Besides deception features, this dataset also includes individual differences information in personality and emotional expression characteristics. It can explore the impact of individual differences on deception behavior. It comprises over 104 hours of deception and emotional videos from 193 subjects. Furthermore, we conducted numerous experiments to provide valuable insights for future deception detection research. MDPE not only supports deception detection, but also provides conditions for tasks such as personality recognition and emotion recognition, and can even study the relationships between them. We believe that MDPE will become a valuable resource for promoting research in the field of affective computing.

cs.CV↗

arXiv2025

Interact with me: Joint Egocentric Forecasting of Intent to Interact, Attitude and Social Actions

For efficient human-agent interaction, an agent should proactively recognize their target user and prepare for upcoming interactions. We formulate this challenging problem as the novel task of jointly forecasting a person's intent to interact with the agent, their attitude towards the agent and the action they will perform, from the agent's (egocentric) perspective. So we propose \emph{SocialEgoNet} - a graph-based spatiotemporal framework that exploits task dependencies through a hierarchical multitask learning approach. SocialEgoNet uses whole-body skeletons (keypoints from face, hands and body) extracted from only 1 second of video input for high inference speed. For evaluation, we augment an existing egocentric human-agent interaction dataset with new class labels and bounding box annotations. Extensive experiments on this augmented dataset, named JPL-Social, demonstrate \emph{real-time} inference and superior performance (average accuracy across all tasks: 83.15\%) of our model outperforming several competitive baselines. The additional annotations and code will be available upon acceptance.

cs.CV↗

arXiv2025

CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification

We propose CLIP-EBC, the first fully CLIP-based model for accurate crowd density estimation. While the CLIP model has demonstrated remarkable success in addressing recognition tasks such as zero-shot image classification, its potential for counting has been largely unexplored due to the inherent challenges in transforming a regression problem, such as counting, into a recognition task. In this work, we investigate and enhance CLIP's ability to count, focusing specifically on the task of estimating crowd sizes from images. Existing classification-based crowd-counting frameworks have significant limitations, including the quantization of count values into bordering real-valued bins and the sole focus on classification errors. These practices result in label ambiguity near the shared borders and inaccurate prediction of count values. Hence, directly applying CLIP within these frameworks may yield suboptimal performance. To address these challenges, we first propose the Enhanced Blockwise Classification (EBC) framework. Unlike previous methods, EBC utilizes integer-valued bins, effectively reducing ambiguity near bin boundaries. Additionally, it incorporates a regression loss based on density maps to improve the prediction of count values. Within our backbone-agnostic EBC framework, we then introduce CLIP-EBC to fully leverage CLIP's recognition capabilities for this task. Extensive experiments demonstrate the effectiveness of EBC and the competitive performance of CLIP-EBC. Specifically, our EBC framework can improve existing classification-based methods by up to 44.5% on the UCF-QNRF dataset, and CLIP-EBC achieves state-of-the-art performance on the NWPU-Crowd test set, with an MAE of 58.2 and an RMSE of 268.5, representing improvements of 8.6% and 13.3% over the previous best method, STEERER. The code and weights are available at https://github.com/Yiming-M/CLIP-EBC.

cs.CV↗

arXiv2024

Explicit evaluation of the $q$-Stokes matrices for certain confluent hypergeometric $q$-difference equations

We prove a connection formula for the basic hypergeomtric function ${}_nφ_{n-1}\left( a_1,...,a_{n-1},0; b_1,...,b_{n-1} ; q, z\right)$ by using the $q$-Borel resummation. As an application, we compute $q$-Stokes matrices of a special confluent hypergeometric $q$-difference system with an irregular singularity. We show that by letting $q\rightarrow 1$, the $q$-Stokes matrices recover the known expressions of the Stokes matrices of the corresponding confluent hypergeometric differential system.

math.CA↗

arXiv2024

ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design

Nature creates diverse proteins through a 'divide and assembly' strategy. Inspired by this idea, we introduce ProteinWeaver, a two-stage framework for protein backbone design. Our method first generates individual protein domains and then employs an SE(3) diffusion model to flexibly assemble these domains. A key challenge lies in the assembling step, given the complex and rugged nature of the inter-domain interaction landscape. To address this challenge, we employ preference alignment to discern complex relationships between structure and interaction landscapes through comparative analysis of generated samples. Comprehensive experiments demonstrate that ProteinWeaver: (1) generates high-quality, novel protein backbones through versatile domain assembly; (2) outperforms RFdiffusion, the current state-of-the-art in backbone design, by 13\% and 39\% for long-chain proteins; (3) shows the potential for cooperative function design through illustrative case studies. To sum up, by introducing a `divide-and-assembly' paradigm, ProteinWeaver advances protein engineering and opens new avenues for functional protein design.

q-bio.BM↗

arXiv2024

Improvements on Permutation Reconstruction from Minors

We study the reconstruction problem of permutation sequences from their $k$-minors, which are subsequences of length $k$ with entries renumbered by $1,2,\ldots,k$ preserving order. We prove that the minimum number $k$ such that any permutation of length $n$ can be reconstructed from the multiset of its $k$-minors is between $\exp{(Ω(\sqrt{\ln n}))}$ and $O(\sqrt{n\ln n})$. These results imply better bounds of a well-studied parameter $N_d$, which is the smallest number such that any permutation of length $n\ge N_d$ can be reconstructed by its $(n-d)$-minors. The new bounds are $ d+\exp(Ω(\sqrt{\ln d}))<N_d<d+O(\sqrt{d\ln d})$ asymptotically, and the previous bounds were $d+\log_2 d<N_d<d^2/4+2d+4$.

math.CO↗

arXiv2024

Natural resources balance sheets accounting: theoretical framework and practice in the Shaanxi province of China

To achieve sustainable development, there is widespread of the need to protect natural resource and improve government oversight in achieving China's economic security and ecological civilization. Compilation of natural resources balance sheet (NRBS) and enhancement of resources management are becoming an important topic in China. How to compile NRBS to affix the responsibility for government and officials for inadequate supervision is still not resolved satisfactorily. This paper proposes the NRBS to enable governments to identify the importance of natural resource restoration and to hold leading cadres accountable for a lack of adequate supervision. The NRBS consist of three accounts: natural resource assets, natural resource liabilities, and net worth. Important components of the NRBS for the liabilities account with a property rights regime are developed to measure and assign responsibility. The compilation of an NRBS is applied to the Chinese province of Shaanxi as an illustration to demonstrate that the accounting framework and the compilation steps are tractable using financial methods and available data. The accounting results of natural resource assets and liabilities unveil the threat to resource management and the policy implications to government and officials. Finally, the advantages and limitations of NRBS are discussed.

econ.GN↗

arXiv2024

ProteinBench: A Holistic Evaluation of Protein Foundation Models

Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics. However, the capabilities and limitations associated with these models remain poorly understood due to the absence of a unified evaluation framework. To fill this gap, we introduce ProteinBench, a holistic evaluation framework designed to enhance the transparency of protein foundation models. Our approach consists of three key components: (i) A taxonomic classification of tasks that broadly encompass the main challenges in the protein domain, based on the relationships between different protein modalities; (ii) A multi-metric evaluation approach that assesses performance across four key dimensions: quality, novelty, diversity, and robustness; and (iii) In-depth analyses from various user objectives, providing a holistic view of model performance. Our comprehensive evaluation of protein foundation models reveals several key findings that shed light on their current capabilities and limitations. To promote transparency and facilitate further research, we release the evaluation dataset, code, and a public leaderboard publicly for further analysis and a general modular toolkit. We intend for ProteinBench to be a living benchmark for establishing a standardized, in-depth evaluation framework for protein foundation models, driving their development and application while fostering collaboration within the field.

q-bio.QM↗

arXiv2024

PuYun: Medium-Range Global Weather Forecasting Using Large Kernel Attention Convolutional Networks

Accurate weather forecasting is essential for understanding and mitigating weather-related impacts. In this paper, we present PuYun, an autoregressive cascade model that leverages large kernel attention convolutional networks. The model's design inherently supports extended weather prediction horizons while broadening the effective receptive field. The integration of large kernel attention mechanisms within the convolutional layers enhances the model's capacity to capture fine-grained spatial details, thereby improving its predictive accuracy for meteorological phenomena. We introduce PuYun, comprising PuYun-Short for 0-5 day forecasts and PuYun-Medium for 5-10 day predictions. This approach enhances the accuracy of 10-day weather forecasting. Through evaluation, we demonstrate that PuYun-Short alone surpasses the performance of both GraphCast and FuXi-Short in generating accurate 10-day forecasts. Specifically, on the 10th day, PuYun-Short reduces the RMSE for Z500 to 720 $m^2/s^2$, compared to 732 $m^2/s^2$ for GraphCast and 740 $m^2/s^2$ for FuXi-Short. Additionally, the RMSE for T2M is reduced to 2.60 K, compared to 2.63 K for GraphCast and 2.65 K for FuXi-Short. Furthermore, when employing a cascaded approach by integrating PuYun-Short and PuYun-Medium, our method achieves superior results compared to the combined performance of FuXi-Short and FuXi-Medium. On the 10th day, the RMSE for Z500 is further reduced to 638 $m^2/s^2$, compared to 641 $m^2/s^2$ for FuXi. These findings underscore the effectiveness of our model ensemble in advancing medium-range weather prediction. Our training code and model will be open-sourced.

cs.LG↗

arXiv2024

Invariant regimes of Spencer scaling law for magnetic compression of rotating FRC plasma

The scaling laws for the magnetic compression of a toroidally rotating field reversed configuration (FRC) have been investigated in this work. The magnetohydrodynamics (MHD) simulations of the magnetic compression on rotating FRCs employing the NIMROD code [C. R. Sovinec \textit{et al.}, J. Comput. Phys. \textbf{195}, 355 (2004)], are compared with the Spencer's one-dimensional (1D) theory [R. L. Spencer \textit{et al.}, Phys. Fluids \textbf{26}, 1564 (1983)] for a wide range of initial flow speeds and profiles. The toroidal flow can influence the scalings directly through the alteration of the compressional work as also evidenced in the 1D adiabatic model, and indirectly by reshaping the initial equilibrium. However, in comparison to the static initial FRC equilibrium cases, the pressure and the radius scalings remain invariant for the magnetic compression ratio $B_{w2}/B_{w1}$ up to 6 in presence of the initial equilibrium flow, suggesting a broader applicable regime of the Spencer scaling law for FRC magnetic compression. The invariant scaling has been proven a natural consequence of the conservation of angular momentum of both fluid and magnetic field during the dynamic compression process.

physics.plasm-ph↗

arXiv2024

Inference via robust optimal transportation: theory and methods

Optimal transportation theory and the related $p$-Wasserstein distance ($W_p$, $p\geq 1$) are widely-applied in statistics and machine learning. In spite of their popularity, inference based on these tools has some issues. For instance, it is sensitive to outliers and it may not be even defined when the underlying model has infinite moments. To cope with these problems, first we consider a robust version of the primal transportation problem and show that it defines the {robust Wasserstein distance}, $W^{(λ)}$, depending on a tuning parameter $λ> 0$. Second, we illustrate the link between $W_1$ and $W^{(λ)}$ and study its key measure theoretic aspects. Third, we derive some concentration inequalities for $W^{(λ)}$. Fourth, we use $W^{(λ)}$ to define minimum distance estimators, we provide their statistical guarantees and we illustrate how to apply the derived concentration inequalities for a data driven selection of $λ$. Fifth, we provide the {dual} form of the robust optimal transportation problem and we apply it to machine learning problems (generative adversarial networks and domain adaptation). Numerical exercises provide evidence of the benefits yielded by our novel methods.

math.ST↗

arXiv2023

Some novel aspects of quantile regression: local stationarity, random forests and optimal transportation

This paper is written for a Festschrift in honour of Professor Marc Hallin and it proposes some developments on quantile regression. We connect our investigation to Marc's scientific production and we present some theoretical and methodological advances for quantiles estimation in non standard settings. We split our contributions in two parts. The first part is about conditional quantiles estimation for nonstationary time series. The second part is about conditional quantiles estimation for the analysis of multivariate independent data in the presence of possibly large dimensional covariates. Monte Carlo studies illustrate numerically the performance of our methods and compare them to some extant techniques.

stat.AP↗